Overview

Dataset statistics

Number of variables12
Number of observations477
Missing cells73
Missing cells (%)1.3%
Duplicate rows11
Duplicate rows (%)2.3%
Total size in memory44.8 KiB
Average record size in memory96.3 B

Variable types

Numeric11
Categorical1

Alerts

Dataset has 11 (2.3%) duplicate rowsDuplicates
personal is highly correlated with joint_redux and 1 other fieldsHigh correlation
welfare is highly correlated with joint_redux and 3 other fieldsHigh correlation
meritocracy is highly correlated with joint_redux and 2 other fieldsHigh correlation
fatalism is highly correlated with joint_redux and 1 other fieldsHigh correlation
joint_redux is highly correlated with personal and 7 other fieldsHigh correlation
joint is highly correlated with personal and 7 other fieldsHigh correlation
sdo is highly correlated with rrsHigh correlation
rwa is highly correlated with joint_redux and 1 other fieldsHigh correlation
pwe is highly correlated with welfare and 2 other fieldsHigh correlation
rrs is highly correlated with welfare and 4 other fieldsHigh correlation
personal is highly correlated with joint_redux and 2 other fieldsHigh correlation
welfare is highly correlated with joint_redux and 3 other fieldsHigh correlation
meritocracy is highly correlated with joint_redux and 2 other fieldsHigh correlation
fatalism is highly correlated with joint_redux and 1 other fieldsHigh correlation
joint_redux is highly correlated with personal and 7 other fieldsHigh correlation
joint is highly correlated with personal and 7 other fieldsHigh correlation
sdo is highly correlated with rrsHigh correlation
rwa is highly correlated with joint_redux and 2 other fieldsHigh correlation
pwe is highly correlated with personal and 4 other fieldsHigh correlation
rrs is highly correlated with welfare and 6 other fieldsHigh correlation
personal is highly correlated with joint_redux and 1 other fieldsHigh correlation
welfare is highly correlated with joint_redux and 1 other fieldsHigh correlation
meritocracy is highly correlated with joint_redux and 1 other fieldsHigh correlation
fatalism is highly correlated with jointHigh correlation
joint_redux is highly correlated with personal and 3 other fieldsHigh correlation
joint is highly correlated with personal and 4 other fieldsHigh correlation
personal is highly correlated with welfare and 8 other fieldsHigh correlation
welfare is highly correlated with personal and 7 other fieldsHigh correlation
meritocracy is highly correlated with personal and 8 other fieldsHigh correlation
fatalism is highly correlated with personal and 7 other fieldsHigh correlation
joint_redux is highly correlated with personal and 8 other fieldsHigh correlation
joint is highly correlated with personal and 8 other fieldsHigh correlation
sdo is highly correlated with personal and 7 other fieldsHigh correlation
rwa is highly correlated with personal and 5 other fieldsHigh correlation
pwe is highly correlated with personal and 8 other fieldsHigh correlation
rrs is highly correlated with personal and 8 other fieldsHigh correlation
age has 63 (13.2%) missing values Missing
education has 10 (2.1%) missing values Missing

Reproduction

Analysis started2022-09-05 22:01:09.646029
Analysis finished2022-09-05 22:01:38.798692
Duration29.15 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

personal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.440251572
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:38.937185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.333333333
Q31.666666667
95-th percentile2.666666667
Maximum4
Range3
Interquartile range (IQR)0.6666666667

Descriptive statistics

Standard deviation0.582166606
Coefficient of variation (CV)0.4042117483
Kurtosis2.160047649
Mean1.440251572
Median Absolute Deviation (MAD)0.3333333333
Skewness1.494611552
Sum687
Variance0.3389179571
MonotonicityNot monotonic
2022-09-05T22:01:39.092492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1237
49.7%
1.66666666774
 
15.5%
1.33333333366
 
13.8%
243
 
9.0%
2.33333333324
 
5.0%
2.66666666719
 
4.0%
38
 
1.7%
3.6666666672
 
0.4%
42
 
0.4%
3.3333333332
 
0.4%
ValueCountFrequency (%)
1237
49.7%
1.33333333366
 
13.8%
1.66666666774
 
15.5%
243
 
9.0%
2.33333333324
 
5.0%
2.66666666719
 
4.0%
38
 
1.7%
3.3333333332
 
0.4%
3.6666666672
 
0.4%
42
 
0.4%
ValueCountFrequency (%)
42
 
0.4%
3.6666666672
 
0.4%
3.3333333332
 
0.4%
38
 
1.7%
2.66666666719
 
4.0%
2.33333333324
 
5.0%
243
 
9.0%
1.66666666774
 
15.5%
1.33333333366
 
13.8%
1237
49.7%

welfare
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.957232704
Minimum1
Maximum4.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:39.265772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.4
median1.8
Q32.4
95-th percentile3.4
Maximum4.4
Range3.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7504726062
Coefficient of variation (CV)0.383435554
Kurtosis0.372239604
Mean1.957232704
Median Absolute Deviation (MAD)0.4
Skewness0.8726454187
Sum933.6
Variance0.5632091327
MonotonicityNot monotonic
2022-09-05T22:01:39.436145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.461
12.8%
1.654
11.3%
153
11.1%
1.852
10.9%
1.242
8.8%
2.241
8.6%
240
8.4%
2.430
6.3%
2.824
 
5.0%
321
 
4.4%
Other values (8)59
12.4%
ValueCountFrequency (%)
153
11.1%
1.242
8.8%
1.461
12.8%
1.654
11.3%
1.852
10.9%
240
8.4%
2.241
8.6%
2.430
6.3%
2.621
 
4.4%
2.824
 
5.0%
ValueCountFrequency (%)
4.43
 
0.6%
4.23
 
0.6%
44
 
0.8%
3.84
 
0.8%
3.64
 
0.8%
3.412
2.5%
3.28
 
1.7%
321
4.4%
2.824
5.0%
2.621
4.4%

meritocracy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.535988819
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:39.619946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.333333333
Q31.666666667
95-th percentile3.333333333
Maximum5
Range4
Interquartile range (IQR)0.6666666667

Descriptive statistics

Standard deviation0.7555721621
Coefficient of variation (CV)0.4919125405
Kurtosis4.831605649
Mean1.535988819
Median Absolute Deviation (MAD)0.3333333333
Skewness2.084637218
Sum732.6666667
Variance0.5708892921
MonotonicityNot monotonic
2022-09-05T22:01:39.806576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1208
43.6%
1.33333333397
20.3%
1.66666666763
 
13.2%
238
 
8.0%
2.33333333326
 
5.5%
2.66666666714
 
2.9%
3.6666666677
 
1.5%
3.3333333337
 
1.5%
36
 
1.3%
4.6666666674
 
0.8%
Other values (3)7
 
1.5%
ValueCountFrequency (%)
1208
43.6%
1.33333333397
20.3%
1.66666666763
 
13.2%
238
 
8.0%
2.33333333326
 
5.5%
2.66666666714
 
2.9%
36
 
1.3%
3.3333333337
 
1.5%
3.6666666677
 
1.5%
43
 
0.6%
ValueCountFrequency (%)
52
 
0.4%
4.6666666674
 
0.8%
4.3333333332
 
0.4%
43
 
0.6%
3.6666666677
 
1.5%
3.3333333337
 
1.5%
36
 
1.3%
2.66666666714
 
2.9%
2.33333333326
5.5%
238
8.0%

fatalism
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.124887691
Minimum1
Maximum4.285714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:40.016234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.142857143
Q11.714285714
median2.142857143
Q32.571428571
95-th percentile3.285714286
Maximum4.285714286
Range3.285714286
Interquartile range (IQR)0.8571428571

Descriptive statistics

Standard deviation0.6587163696
Coefficient of variation (CV)0.3100005579
Kurtosis0.0599341031
Mean2.124887691
Median Absolute Deviation (MAD)0.4285714286
Skewness0.4638699764
Sum1013.571429
Variance0.4339072556
MonotonicityNot monotonic
2022-09-05T22:01:40.209521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.14285714343
 
9.0%
2.42857142941
 
8.6%
1.71428571441
 
8.6%
240
 
8.4%
1.85714285736
 
7.5%
2.28571428633
 
6.9%
2.57142857132
 
6.7%
1.57142857132
 
6.7%
1.42857142926
 
5.5%
122
 
4.6%
Other values (14)131
27.5%
ValueCountFrequency (%)
122
4.6%
1.14285714317
 
3.6%
1.28571428620
4.2%
1.42857142926
5.5%
1.57142857132
6.7%
1.71428571441
8.6%
1.85714285736
7.5%
240
8.4%
2.14285714343
9.0%
2.28571428633
6.9%
ValueCountFrequency (%)
4.2857142861
 
0.2%
4.1428571433
 
0.6%
42
 
0.4%
3.8571428572
 
0.4%
3.7142857141
 
0.2%
3.5714285711
 
0.2%
3.4285714299
1.9%
3.28571428610
2.1%
3.14285714312
2.5%
317
3.6%

joint_redux
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct110
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.644491032
Minimum1
Maximum4.244444444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:40.420692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.222222222
median1.511111111
Q31.933333333
95-th percentile2.76
Maximum4.244444444
Range3.244444444
Interquartile range (IQR)0.7111111111

Descriptive statistics

Standard deviation0.5425088001
Coefficient of variation (CV)0.3298946541
Kurtosis2.08317755
Mean1.644491032
Median Absolute Deviation (MAD)0.3333333333
Skewness1.264772696
Sum784.4222222
Variance0.2943157982
MonotonicityNot monotonic
2022-09-05T22:01:40.667226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138
 
8.0%
1.13333333326
 
5.5%
1.06666666721
 
4.4%
1.218
 
3.8%
1.31111111115
 
3.1%
1.412
 
2.5%
1.42222222212
 
2.5%
1.26666666712
 
2.5%
1.48888888911
 
2.3%
1.611
 
2.3%
Other values (100)301
63.1%
ValueCountFrequency (%)
138
8.0%
1.06666666721
4.4%
1.1111111116
 
1.3%
1.13333333326
5.5%
1.1777777787
 
1.5%
1.218
3.8%
1.2222222227
 
1.5%
1.2444444446
 
1.3%
1.26666666712
 
2.5%
1.2888888891
 
0.2%
ValueCountFrequency (%)
4.2444444441
 
0.2%
4.1333333331
 
0.2%
3.5555555561
 
0.2%
3.41
 
0.2%
3.3555555561
 
0.2%
3.3111111111
 
0.2%
3.21
 
0.2%
3.0666666671
 
0.2%
3.0666666671
 
0.2%
34
0.8%

joint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct403
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.905522612
Minimum1
Maximum4.337142857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:40.899188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.151428571
Q11.50952381
median1.866666667
Q32.198095238
95-th percentile2.852380952
Maximum4.337142857
Range3.337142857
Interquartile range (IQR)0.6885714286

Descriptive statistics

Standard deviation0.5221128378
Coefficient of variation (CV)0.2739998122
Kurtosis1.044753902
Mean1.905522612
Median Absolute Deviation (MAD)0.3485714286
Skewness0.7499837521
Sum908.9342857
Variance0.2726018154
MonotonicityNot monotonic
2022-09-05T22:01:41.120965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16
 
1.3%
1.1333333334
 
0.8%
2.8523809523
 
0.6%
2.4466666673
 
0.6%
1.3133333333
 
0.6%
1.3733333333
 
0.6%
2.3323809523
 
0.6%
1.2571428573
 
0.6%
1.5009523813
 
0.6%
2.6552380953
 
0.6%
Other values (393)443
92.9%
ValueCountFrequency (%)
16
1.3%
1.0333333331
 
0.2%
1.041
 
0.2%
1.0571428571
 
0.2%
1.0619047621
 
0.2%
1.0685714291
 
0.2%
1.0971428571
 
0.2%
1.11
 
0.2%
1.1190476191
 
0.2%
1.121
 
0.2%
ValueCountFrequency (%)
4.3371428571
0.2%
4.0514285711
0.2%
3.4438095241
0.2%
3.3952380951
0.2%
3.3580952381
0.2%
3.3371428571
0.2%
3.3257142861
0.2%
3.2019047621
0.2%
3.0838095241
0.2%
3.0819047621
0.2%

sdo
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.658280922
Minimum1
Maximum3.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:41.308721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile3
Maximum3.75
Range2.75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6279130271
Coefficient of variation (CV)0.3786529886
Kurtosis-0.08111120821
Mean1.658280922
Median Absolute Deviation (MAD)0.5
Skewness0.8185388204
Sum791
Variance0.3942747697
MonotonicityNot monotonic
2022-09-05T22:01:41.465279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1134
28.1%
270
14.7%
1.2567
14.0%
1.558
12.2%
1.7551
 
10.7%
2.2531
 
6.5%
2.520
 
4.2%
319
 
4.0%
2.7517
 
3.6%
3.257
 
1.5%
Other values (2)3
 
0.6%
ValueCountFrequency (%)
1134
28.1%
1.2567
14.0%
1.558
12.2%
1.7551
 
10.7%
270
14.7%
2.2531
 
6.5%
2.520
 
4.2%
2.7517
 
3.6%
319
 
4.0%
3.257
 
1.5%
ValueCountFrequency (%)
3.751
 
0.2%
3.52
 
0.4%
3.257
 
1.5%
319
 
4.0%
2.7517
 
3.6%
2.520
 
4.2%
2.2531
6.5%
270
14.7%
1.7551
10.7%
1.558
12.2%

rwa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.492662474
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:41.631400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6685056687
Coefficient of variation (CV)0.4478612415
Kurtosis2.658448082
Mean1.492662474
Median Absolute Deviation (MAD)0
Skewness1.589266033
Sum712
Variance0.4468998291
MonotonicityNot monotonic
2022-09-05T22:01:41.794963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1245
51.4%
1.5106
22.2%
260
 
12.6%
2.537
 
7.8%
318
 
3.8%
3.57
 
1.5%
43
 
0.6%
51
 
0.2%
ValueCountFrequency (%)
1245
51.4%
1.5106
22.2%
260
 
12.6%
2.537
 
7.8%
318
 
3.8%
3.57
 
1.5%
43
 
0.6%
51
 
0.2%
ValueCountFrequency (%)
51
 
0.2%
43
 
0.6%
3.57
 
1.5%
318
 
3.8%
2.537
 
7.8%
260
 
12.6%
1.5106
22.2%
1245
51.4%

pwe
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.568134172
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:41.970859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2.5
Maximum4.5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6268428954
Coefficient of variation (CV)0.3997380496
Kurtosis2.321938368
Mean1.568134172
Median Absolute Deviation (MAD)0.5
Skewness1.327563727
Sum748
Variance0.3929320156
MonotonicityNot monotonic
2022-09-05T22:01:42.127855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1190
39.8%
1.5129
27.0%
298
20.5%
2.537
 
7.8%
315
 
3.1%
3.54
 
0.8%
42
 
0.4%
4.52
 
0.4%
ValueCountFrequency (%)
1190
39.8%
1.5129
27.0%
298
20.5%
2.537
 
7.8%
315
 
3.1%
3.54
 
0.8%
42
 
0.4%
4.52
 
0.4%
ValueCountFrequency (%)
4.52
 
0.4%
42
 
0.4%
3.54
 
0.8%
315
 
3.1%
2.537
 
7.8%
298
20.5%
1.5129
27.0%
1190
39.8%

rrs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.564640112
Minimum1
Maximum4.333333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:42.298152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.333333333
Q32
95-th percentile3
Maximum4.333333333
Range3.333333333
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6616252605
Coefficient of variation (CV)0.422860986
Kurtosis1.238147833
Mean1.564640112
Median Absolute Deviation (MAD)0.3333333333
Skewness1.312682929
Sum746.3333333
Variance0.4377479853
MonotonicityNot monotonic
2022-09-05T22:01:42.458843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1183
38.4%
1.33333333395
19.9%
1.66666666776
15.9%
2.33333333338
 
8.0%
236
 
7.5%
2.66666666718
 
3.8%
314
 
2.9%
3.33333333311
 
2.3%
3.6666666675
 
1.0%
4.3333333331
 
0.2%
ValueCountFrequency (%)
1183
38.4%
1.33333333395
19.9%
1.66666666776
15.9%
236
 
7.5%
2.33333333338
 
8.0%
2.66666666718
 
3.8%
314
 
2.9%
3.33333333311
 
2.3%
3.6666666675
 
1.0%
4.3333333331
 
0.2%
ValueCountFrequency (%)
4.3333333331
 
0.2%
3.6666666675
 
1.0%
3.33333333311
 
2.3%
314
 
2.9%
2.66666666718
 
3.8%
2.33333333338
 
8.0%
236
 
7.5%
1.66666666776
15.9%
1.33333333395
19.9%
1183
38.4%

age
Real number (ℝ≥0)

MISSING

Distinct54
Distinct (%)13.0%
Missing63
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean39.59661836
Minimum10
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2022-09-05T22:01:42.656636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile23
Q130
median37
Q348
95-th percentile60
Maximum87
Range77
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.38796136
Coefficient of variation (CV)0.3128540232
Kurtosis0.4865827442
Mean39.59661836
Median Absolute Deviation (MAD)8
Skewness0.8002425471
Sum16393
Variance153.4615866
MonotonicityNot monotonic
2022-09-05T22:01:42.882399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3022
 
4.6%
3320
 
4.2%
3117
 
3.6%
3717
 
3.6%
3816
 
3.4%
3415
 
3.1%
2915
 
3.1%
3615
 
3.1%
2815
 
3.1%
2614
 
2.9%
Other values (44)248
52.0%
(Missing)63
 
13.2%
ValueCountFrequency (%)
101
 
0.2%
203
 
0.6%
211
 
0.2%
227
1.5%
2312
2.5%
246
 
1.3%
258
1.7%
2614
2.9%
277
1.5%
2815
3.1%
ValueCountFrequency (%)
871
0.2%
861
0.2%
762
0.4%
752
0.4%
711
0.2%
701
0.2%
692
0.4%
672
0.4%
662
0.4%
642
0.4%

education
Categorical

MISSING

Distinct9
Distinct (%)1.9%
Missing10
Missing (%)2.1%
Memory size3.9 KiB
Master's degree
216 
Bachelor's degree
141 
Doctoral degree
51 
Professional degree
35 
Some college
 
9
Other values (4)
 
15

Length

Max length28
Median length15
Mean length15.85867238
Min length5

Characters and Unicode

Total characters7406
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowMaster's degree
2nd rowMaster's degree
3rd rowMaster's degree
4th rowBachelor's degree
5th rowBachelor's degree

Common Values

ValueCountFrequency (%)
Master's degree216
45.3%
Bachelor's degree141
29.6%
Doctoral degree51
 
10.7%
Professional degree35
 
7.3%
Some college9
 
1.9%
Associate degree8
 
1.7%
High school graduate3
 
0.6%
Other3
 
0.6%
Less than high school degree1
 
0.2%
(Missing)10
 
2.1%

Length

2022-09-05T22:01:43.102323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-05T22:01:43.308123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
degree452
48.2%
master's216
23.1%
bachelor's141
 
15.0%
doctoral51
 
5.4%
professional35
 
3.7%
some9
 
1.0%
college9
 
1.0%
associate8
 
0.9%
high4
 
0.4%
school4
 
0.4%
Other values (4)8
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e1790
24.2%
r901
12.2%
s665
 
9.0%
470
 
6.3%
g468
 
6.3%
a458
 
6.2%
d455
 
6.1%
'357
 
4.8%
o347
 
4.7%
t282
 
3.8%
Other values (17)1213
16.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6112
82.5%
Space Separator470
 
6.3%
Uppercase Letter467
 
6.3%
Other Punctuation357
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1790
29.3%
r901
14.7%
s665
 
10.9%
g468
 
7.7%
a458
 
7.5%
d455
 
7.4%
o347
 
5.7%
t282
 
4.6%
l249
 
4.1%
c213
 
3.5%
Other values (6)284
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
M216
46.3%
B141
30.2%
D51
 
10.9%
P35
 
7.5%
S9
 
1.9%
A8
 
1.7%
H3
 
0.6%
O3
 
0.6%
L1
 
0.2%
Space Separator
ValueCountFrequency (%)
470
100.0%
Other Punctuation
ValueCountFrequency (%)
'357
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6579
88.8%
Common827
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1790
27.2%
r901
13.7%
s665
 
10.1%
g468
 
7.1%
a458
 
7.0%
d455
 
6.9%
o347
 
5.3%
t282
 
4.3%
l249
 
3.8%
M216
 
3.3%
Other values (15)748
11.4%
Common
ValueCountFrequency (%)
470
56.8%
'357
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII7406
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1790
24.2%
r901
12.2%
s665
 
9.0%
470
 
6.3%
g468
 
6.3%
a458
 
6.2%
d455
 
6.1%
'357
 
4.8%
o347
 
4.7%
t282
 
3.8%
Other values (17)1213
16.4%

Interactions

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Correlations

2022-09-05T22:01:43.468497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-05T22:01:43.693961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-05T22:01:44.315294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-05T22:01:44.557593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-05T22:01:37.919838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-05T22:01:38.268959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-05T22:01:38.492852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-05T22:01:38.616267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

personalwelfaremeritocracyfatalismjoint_reduxjointsdorwapwerrsageeducation
01.0000003.04.6666672.4285712.8888892.8523811.502.02.01.66666749.0Master's degree
11.0000003.04.6666672.4285712.8888892.8523811.502.02.01.66666749.0Master's degree
21.0000001.61.6666672.1428571.4222221.7485711.001.01.51.00000039.0Master's degree
31.3333331.41.3333332.1428571.3555561.6752381.002.52.02.000000NaNBachelor's degree
41.6666671.41.3333332.4285711.4666671.8323811.001.01.01.00000026.0Bachelor's degree
52.3333331.21.0000002.2857141.5111112.0971431.251.01.52.33333330.0Bachelor's degree
62.0000002.01.0000002.2857141.6666672.0904761.251.01.51.00000034.0Master's degree
71.6666672.01.0000002.0000001.5555561.9333331.001.51.01.00000023.0Bachelor's degree
82.3333332.01.0000004.0000001.7777782.4000002.501.52.51.33333329.0Bachelor's degree
93.0000002.61.3333332.7142862.3111112.5961902.252.52.52.33333343.0Bachelor's degree

Last rows

personalwelfaremeritocracyfatalismjoint_reduxjointsdorwapwerrsageeducation
4671.0000001.01.0000001.0000001.0000001.0000003.001.01.02.33333341.0Doctoral degree
4681.0000001.22.6666673.5714291.6222221.9209522.503.02.54.33333334.0Bachelor's degree
4691.0000001.01.0000001.5714291.0000001.1809521.001.01.01.00000050.0Master's degree
4701.0000001.82.0000001.8571431.6000001.6314291.751.01.52.33333347.0Some college
4711.0000001.01.0000001.8571431.0000001.2380951.001.01.51.00000050.0Master's degree
4724.0000004.44.3333334.2857144.2444444.3371433.004.04.53.333333NaNNaN
4732.6666672.02.3333332.1428572.3333332.2952382.002.52.03.000000NaNNaN
4741.0000001.01.3333331.1428571.1111111.1285713.251.01.02.33333322.0High school graduate
4752.6666672.22.6666673.4285712.5111112.9257142.751.04.53.666667NaNNaN
4761.0000004.25.0000002.7142863.4000002.9161902.003.01.01.00000037.0Bachelor's degree

Duplicate rows

Most frequently occurring

personalwelfaremeritocracyfatalismjoint_reduxjointsdorwapwerrsageeducation# duplicates
71.6666672.82.3333334.1428572.2666672.6552382.251.51.52.33333333.0Master's degree3
01.0000001.01.0000001.2857141.0000001.1904761.001.01.01.00000034.0Master's degree2
11.0000001.41.0000001.0000001.1333331.3133331.001.01.51.00000057.0Master's degree2
21.0000001.61.3333331.7142861.3111111.6628571.251.01.52.00000037.0Master's degree2
31.0000001.81.3333331.1428571.3777781.4885711.001.01.51.33333330.0Bachelor's degree2
41.0000002.21.3333331.7142861.5111111.9161902.001.02.01.00000040.0Doctoral degree2
51.0000003.04.6666672.4285712.8888892.8523811.502.02.01.66666749.0Master's degree2
61.3333331.81.3333331.7142861.4888891.6028571.001.01.51.00000022.0Bachelor's degree2
82.0000001.41.0000003.1428571.4666671.8752383.252.53.01.00000031.0Master's degree2
92.0000003.41.3333333.0000002.2444442.4466672.002.53.02.00000029.0Bachelor's degree2